The problem Urban Seva solves

Urban Seva is a community-centric solution designed to empower citizens & streamline collaboration with local govt. authorities. Through the app, citizens can report various civic issues, such as damaged roads, verified through image recognition & crowdsourcing. Users can enjoy real-time issue updates, explore an interactive map, access a dynamic dashboard, & engage with a gamified point system. The web app allows the govt. to update issue status, send alerts & view advanced analysis of issues.

For Citizens:

1. Issue Reporting: Allows citizens to report various local issues around them
2. Issue Verification: Employs image recognition for reported issue, time-stamping, live images, and crowd validation to ensure the accuracy and legitimacy of reported issues.
3. Real-Time Issue Updates regarding the status of reported issues, keeping them informed of developments.
4. Interactive Map: Allows users to view all reported issues in their locality on an interactive map.
5. Comprehensive Dashboard: Offers a comprehensive dashboard for users to track and manage issues in specific areas.
6. Community Engagement: Facilitates community engagement by enabling users to comment on, upvote, and downvote reported issues, promoting collaboration and involvement.
7. Government Alerts: Enables citizens to receive government alerts about disruptions of certain facilities.

For Government Agencies:

1. Update Issue Status
2. Region-Based and Category-Based Issue Viewing
3. Enhances understanding of trends and patterns within the reported issues through data visualization tools.
4. Interactive Map: To visually represent issues in different locations.
5. Issue Alerts: The Government can send alerts to citizens about facility disruptions.
6. Issue Resolution Tracking
7. Task Assignment and Management: To specific departments of the government.

Challenges we ran into

1. Verification of Reported Issues: Verifying the legitimacy and accuracy of reported issues was critical to maintaining the platform's credibility. Citizens and government agencies needed to trust the data. To overcome this challenge, we took a multifaceted approach. Firstly, we employed advanced image recognition algorithms to analyze the images submitted with issue reports automatically. Secondly, we integrated crowd validation, allowing users to vote and comment on reported issues. Furthermore, we considered the location and time stamp of the captured image to ensure issue validity.

2. Real-Time Updates: Delivering real-time updates to users while maintaining system performance was a technical challenge. We optimized data synchronization, used efficient real-time databases, and leveraged content delivery networks to reduce latency in delivering updates.

3. Image Classification Accuracy: Achieving high accuracy in image classification for issue verification was a complex task. Through continuous training, refinement of our learning models, and increasing the dataset, we improved image classification accuracy, reducing false positives and negatives.